The disclosed embodiments relate generally to an approach for improving the operability of a print production environment and, more particularly to a method and system applicable to an environment where print jobs that recur in a cyclic pattern are processed.
Document production environments, such as print shops, convert printing orders, such as print jobs, into finished printed material. A print shop may process print jobs using resources such as printers, cutters, collators and other similar equipment. Typically, resources in print shops are organized such that when a print job arrives from a customer at a particular print shop, the print job can be processed by performing one or more production functions.
In one example of print shop operation, product variety (e.g., the requirements of a given job) can be low, and the associated steps for a significant number of jobs might consist of printing, inserting, sorting and shipping. In another example, product variety (corresponding, for instance, with job size) can be quite high and the equipment used to process these jobs (e.g. continuous feed machines and inserting equipment) can require a high changeover time. Experience working with some very large print shops has revealed that print demand exhibits a tremendous variety of time series behavior. High variability in such large print shop environments can result from large volumes, and may be manifested in what is sometimes referred to as “fat-tailed” or “heavy-tailed” distributions.
Forecasting demand for a given large print shop can be useful in, among other things, managing shop resources. In one approach, as described by the above-referenced U.S. patent application Ser. No. 11/868,993, a time series (representing total demand) is disaggregated into at least two demand components with one of the demand components having a first variability level and another demand component having a second variability. One forecasting technique may then be applied to the one demand component with the first variability level and another forecasting technique to the other demand component with the second variability level. In turn, the forecasted demand components may be re-aggregated to obtain a forecast for total demand.
While this approach is well suited for its intended purpose, it appears to contemplate uniform sampling, throughout disaggregation, forecasting and aggregation, with the same sampling interval. Using the same sampling interval throughout, however, can make forecasting of a given demand component needlessly difficult. That is, it might be difficult to perform forecasting for a demand component series using the same sampling interval as the total demand time series from which the demand component series was extracted.
In one aspect of the disclosed embodiments there is disclosed a print demand forecasting system for use with a print production system in which multiple print jobs are processed over a selected time interval. The print demand forecasting system includes: a data collection tool, said data collection tool collecting print demand data for each print job processed during the selected time interval, wherein the print demand data comprises a set of aggregated demand related points corresponding with a first time scale; a memory; and a computer implemented service manager for processing the stored set of aggregated demand related points to obtain a first demand component and a second demand component, the first demand component including a first set of demand component related points and the second demand component including a second set of demand component related points, wherein each one of a total number of demand component related points in the first set of demand component related points and a total number of demand component related points in the second set of demand component related points is less than a total number of aggregated demand related points, said memory comprising one or more programming instructions that, when executed, instruct said computer implemented service manager to: process the first set of demand component related points in such a way that at least some of the demand component related points of the first set of demand component related points are corresponded with a second time scale, forecast a demand component related point with the first set of demand component related points corresponded with the second time scale, and correspond both the forecasted demand component related point and the first set of demand related points corresponded with the second time scale with the first time scale to obtain a third set of demand component related points.
In another aspect of the disclosed embodiments there is disclosed a print demand forecasting method for use with a print production system in which multiple print jobs are processed over a selected time interval. The print demand forecasting method includes: A. using a processor to process a stored set of aggregated demand related points, corresponding with a first time scale, to obtain a first demand component and a second demand component, the first demand component including a first set of demand component related points and the second demand component including a second set of demand component related points, wherein each one of a total number of demand component related points in the first set of demand component related points and a total number of demand component related points in the second set of demand component related points is less than a total number of aggregated demand related points; and B. using the processor to (1) process the first set of demand component related points in such a way that the that at least some of the demand component related points of the first set of demand component related points are corresponded with a second time scale, (2) forecast a demand component related point with the first set of demand component related points corresponded with the second time scale, and (3) correspond both the forecasted demand component related point and the first set of demand related points corresponded with the second time scale with the first time scale to obtain a third set of demand component related points.